1. Field of the Invention
The present invention generally relates to methods and systems for generating simulated output for a specimen using a learning based model that is configured for mapping a triangular relationship between optical images, electron beam images, and design data.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Fabricating semiconductor devices such as logic and memory devices typically includes processing a substrate such as a semiconductor wafer using a large number of semiconductor fabrication processes to form various features and multiple levels of the semiconductor devices. For example, lithography is a semiconductor fabrication process that involves transferring a pattern from a reticle to a resist arranged on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing (CMP), etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated in an arrangement on a single semiconductor wafer and then separated into individual semiconductor devices.
Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on specimens to drive higher yield in the manufacturing process and thus higher profits. Inspection has always been an important part of fabricating semiconductor devices. However, as the dimensions of semiconductor devices decrease, inspection becomes even more important to the successful manufacture of acceptable semiconductor devices because smaller defects can cause the devices to fail.
Defect review typically involves re-detecting defects detected as such by an inspection process and generating additional information about the defects at a higher resolution using either a high magnification optical system or a scanning electron microscope (SEM). Defect review is therefore performed at discrete locations on specimens where defects have been detected by inspection. The higher resolution data for the defects generated by defect review is more suitable for determining attributes of the defects such as profile, roughness, more accurate size information, etc.
Metrology processes are also used at various steps during a semiconductor manufacturing process to monitor and control the process. Metrology processes are different than inspection processes in that, unlike inspection processes in which defects are detected on specimens, metrology processes are used to measure one or more characteristics of the specimens that cannot be determined using currently used inspection tools. For example, metrology processes are used to measure one or more characteristics of specimens such as a dimension (e.g., line width, thickness, etc.) of features formed on the specimens during a process such that the performance of the process can be determined from the one or more characteristics. In addition, if the one or more characteristics of the specimens are unacceptable (e.g., out of a predetermined range for the characteristic(s)), the measurements of the one or more characteristics of the specimens may be used to alter one or more parameters of the process such that additional specimens manufactured by the process have acceptable characteristic(s).
Metrology processes are also different than defect review processes in that, unlike defect review processes in which defects that are detected by inspection are re-visited in defect review, metrology processes may be performed at locations at which no defect has been detected. In other words, unlike defect review, the locations at which a metrology process is performed on specimens may be independent of the results of an inspection process performed on the specimens. In particular, the locations at which a metrology process is performed may be selected independently of inspection results.
As described above, therefore, different information may be generated for a single specimen. This information may include design information for the specimen (i.e., information for a design of devices being formed on the specimen), optical image(s) generated for the specimen by one or more tools (e.g., an inspection tool), electron beam image(s) generated for the specimen by one or more tools (e.g., a defect review tool). It can sometimes be helpful to use a combination of different information to perform one or more processes on or for the specimen and/or to determine further information for the specimen. For example, it may be helpful to have optical and electron beam images corresponding to a single location within a design of a specimen to help diagnose a problem identified in that location within the design.
Using different types of information for a specimen in combination therefore requires some mapping of one type of information to another. Oftentimes, currently, such mapping may be performed by aligning different actual images generated for a specimen to each other (e.g., using alignment features in the images and/or on the specimen and/or aligning the different actual images to a common reference (e.g., design)). However, due to differences between the different types of information (e.g., different resolutions, pixel sizes, imaging methods (such as optical vs. electron beam), etc.), alignment of one type of information to another to establish a mapping between the different types of information can be relatively difficult and is susceptible to errors in the alignment method and/or algorithm and noise sources on the specimen (e.g., color variation). In addition, often, the different types of information that are used in combination must be generated separately and independently of each other. For example, in order to establish a relationship between optical and electron beam images for a specimen, the actual optical and electron beam images may need to be generated by imaging a physical version of the specimen. In addition, to establish a relationship between such images and design information for the specimen, the design information may be needed and may not always be available. Therefore, the currently used methods and systems for determining a mapping between different types of information for specimens such as reticles and wafers can be time consuming, expensive due to needing the specimen for actual imaging and imaging tools for generating the images, error prone due to the noise and other errors in the mapping itself, and even impossible when the design information is not available.
Accordingly, it would be advantageous to develop systems and methods for mapping different types of information for a specimen to each other that do not have one or more of the disadvantages described above.